Leading a mentally stimulating life may build up a reserve of neural and mental resources that preserve cognitive abilities in late life. Recent autopsy evidence links neuronal density in the locus coeruleus (LC), the brain's main source of norepinephrine, to slower cognitive decline before death, inspiring the idea that the noradrenergic system is a key component of reserve. Here, we tested this hypothesis using neuromelanin-sensitive magnetic resonance imaging to visualize and measure LC signal intensity in healthy younger and older adults. Established proxies of reserve, including education, occupational attainment, and verbal intelligence, were linearly correlated with LC signal intensity in both age groups. Results indicated that LC signal intensity was significantly higher in older than younger adults and significantly lower in women than in men. Consistent with the LC-reserve hypothesis, both verbal intelligence and a composite reserve score were positively associated with LC signal intensity in older adults. LC signal intensity was also more strongly associated with attentional shifting ability in older adults with lower cognitive reserve. Together these findings link in vivo estimates of LC neuromelanin signal intensity to cognitive reserve in normal aging.

No abstract

A salient characteristic of monkey inferior temporal (IT) cortex is the IT face processing network. Its hallmarks include: “face neurons” that respond more to faces than non-face objects, strong spatial clustering of those neurons in foci at each IT anatomical level (“face patches”), and the preferential interconnection of those foci. While some deep artificial neural networks (ANNs) are good predictors of IT neuronal responses, including face neurons, they do not explain those face network hallmarks. Here we ask if they might be explained with a simple, metabolically motivated addition to current ANN ventral stream models. Specifically, we designed and successfully trained topographic deep ANNs (TDANNs) to solve real-world visual recognition tasks (as in prior work), but, in addition, we also optimized each network to minimize a proxy for neuronal wiring length within its IT layers. We report that after this dual optimization, the model IT layers of TDANNs reproduce the hallmarks of the IT face network: the presence of face neurons, clusters of face neurons that quantitatively match those found in IT face patches, connectivity between those patches, and the emergence of face viewpoint invariance along the network hierarchy. We find that these phenomena emerge for a range of naturalistic experience, but not for highly unnatural training. Taken together, these results show that the IT face processing network could be a consequence of a basic hierarchical anatomy along the ventral stream, selection pressure on the visual system to accomplish general object categorization, and selection pressure to minimize axonal wiring length.

It is widely accepted that after the first cortical visual area, V1, a series of stages achieves a representation of complex shapes, such as faces and objects, so that they can be understood and recognized. A major challenge for the study of complex shape perception has been the lack of a principled basis for scaling of the physical differences between stimuli so that their similarity can be specified, unconfounded by early-stage differences. Without the specification of such similarities, it is difficult to make sound inferences about the contributions of later stages to neural activity or psychophysical performance. A Web-based app is described that is based on the Malsburg Gabor-jet model (Lades et al., 1993), which allows easy specification of the V1 similarity of pairs of stimuli, no matter how intricate. The model predicts the psycho physical discriminability of metrically varying faces and complex blobs almost perfectly (Yue, Biederman, Mangini, von der Malsburg, & Amir, 2012), and serves as the input stage of a large family of contemporary neurocomputational models of vision.Keywords 2-D shape and form . Similarity . Face perception Consider the problem of determining whether people or monkeys are more sensitive to differences in nonaccidental properties (NAPs)-whether a contour is straight or curved, for example-than to differences in metric properties (MPs)-such as differences in degrees of curvature. If we assume that the sensitivity to differences in NAPs arises at a stage in the ventral pathway later than V1, how can the physical properties of the stimuli be selected in a principled manner, so that the comparisons are not confounded with differences in V1 activation? The same methodological problem arises if an investigator wishes to determine whether observers are more sensitive to differences in facial expression than to differences in identity (or sex, or orientation in depth, etc.). This problem arises not only in psychophysical scaling of stimuli, but also with studies designed to more directly reflect the underlying neural correlates, such as fMRI fastadaptation designs and single-unit recordings. It can be argued that this problem of the scaling of shape similarity had been a major reason why, despite shape being the major input into visual cognition, the rigorous study of shape perception had clearly lagged the study of other perceptual attributes, such as color, motion, or stereo. The value of an intuitive implementation of the Gabor-jet modelDespite the utility in employing such a scaling system, the Gaborjet model is somewhat mathematically dense and cumbersome to explain to the uninitiated, thus diminishing its accessibility. Here, we introduce a Web-based applet designed to provide an engaging, graphically oriented guided tour of the model. The applet allows users to upload their own images, observe the transformations and computations made by the algorithm, customize the visualization of different processes, and retrieve a ranking of dissimilarity values for pairs of images. S...

Task-optimized convolutional neural networks (CNNs) show striking similarities to the ventral visual stream. However, human-imperceptible image perturbations can cause a CNN to make incorrect predictions. Here we provide insight into this brittleness by investigating the representations of models that are either robust or not robust to image perturbations. Theory suggests that the robustness of a system to these perturbations could be related to the power law exponent of the eigenspectrum of its set of neural responses, where power law exponents closer to and larger than one would indicate a system that is less susceptible to input perturbations. We show that neural responses in mouse and macaque primary visual cortex (V1) obey the predictions of this theory, where their eigenspectra have power law exponents of at least one. We also find that the eigenspectra of model representations decay slowly relative to those observed in neurophysiology and that robust models have eigenspectra that decay slightly faster and have higher power law exponents than those of non-robust models. The slow decay of the eigenspectra suggests that substantial variance in the model responses is related to the encoding of fine stimulus features. We therefore investigated the spatial frequency tuning of artificial neurons and found that a large proportion of them preferred high spatial frequencies and that robust models had preferred spatial frequency distributions more aligned with the measured spatial frequency distribution of macaque V1 cells. Furthermore, robust models were quantitatively better models of V1 than non-robust models. Our results are consistent with other findings that there is a misalignment between human and machine perception. They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.

Human ventral temporal cortex (VTC) is critical for visual recognition. It is thought that this ability is supported by large-scale patterns of activity across VTC that contain information about visual categories. However, it is unknown how category representations in VTC are organized at the submillimeter scale and across cortical depths. To fill this gap in knowledge, we measured BOLD responses in medial and lateral VTC to images spanning 10 categories from five domains (written characters, bodies, faces, places, and objects) at an ultra-high spatial resolution of 0.8 mm using 7 Tesla fMRI in both male and female participants. Representations in lateral VTC were organized most strongly at the general level of domains (e.g., places), whereas medial VTC was also organized at the level of specific categories (e.g., corridors and houses within the domain of places). In both lateral and medial VTC, domain-level and category-level structure decreased with cortical depth, and downsampling our data to standard resolution (2.4 mm) did not reverse differences in representations between lateral and medial VTC. The functional diversity of representations across VTC partitions may allow downstream regions to read out information in a flexible manner according to task demands. These results bridge an important gap between electrophysiological recordings in single neurons at the micron scale in nonhuman primates and standard-resolution fMRI in humans by elucidating distributed responses at the submillimeter scale with ultra-high-resolution fMRI in humans.

Task-optimized convolutional neural networks (CNNs) show striking similarities to the ventral visual stream. However, human-imperceptible image perturbations can cause a CNN to make incorrect predictions. Here we provide insight into this brittleness by investigating the representations of models that are either robust or not robust to image perturbations. Theory suggests that the robustness of a system to these perturbations could be related to the power law exponent of the eigenspectrum of its set of neural responses, where power law exponents closer to and larger than one would indicate a system that is less susceptible to input perturbations. We show that neural responses in mouse and macaque primary visual cortex (V1) obey the predictions of this theory, where their eigenspectra have power law exponents of at least one. We also find that the eigenspectra of model representations decay slowly relative to those observed in neurophysiology and that robust models have eigenspectra that decay slightly faster and have higher power law exponents than those of non-robust models. The slow decay of the eigenspectra suggests that substantial variance in the model responses is related to the encoding of fine stimulus features. We therefore investigated the spatial frequency tuning of artificial neurons and found that a large proportion of them preferred high spatial frequencies and that robust models had preferred spatial frequency distributions more aligned with the measured spatial frequency distribution of macaque V1 cells. Furthermore, robust models were quantitatively better models of V1 than non-robust models. Our results are consistent with other findings that there is a misalignment between human and machine perception. They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.

The lateral occipital complex (LOC), the cortical region critical for shape perception, is localized with fMRI by its greater BOLD activity when viewing intact objects compared with their scrambled versions (resembling texture). Despite hundreds of studies investigating LOC, what the LOC localizer accomplishes-beyond distinguishing shape from texture-has never been resolved. By independently scattering the intact parts of objects, the axis structure defining the relations between parts was no longer defined. This led to a diminished BOLD response, despite the increase in the number of independent entities (the parts) produced by the scattering, thus indicating that LOC specifies interpart relations, in addition to specifying the shape of the parts themselves. LOC's sensitivity to relations is not confined to those between parts but is also readily apparent between objects, rendering it-and not subsequent "place" areas-as the critical region for the representation of scenes. Moreover, that these effects are witnessed with novel as well as familiar intact objects and scenes suggests that the relations are computed on the fly, rather than being retrieved from memory.